DRAM example narrative
Michael Shaffer
Generated December 10, 2021

DRAM example narrative

DRAM on KBase let's anyone run annotations using DRAM in the cloud. DRAM is an annotation tool that can annotate bacterial, archaeal and viral genomes and distills those annotatios into represetations of the functional genomic potential of those organisms. If you want to read more about DRAM you can check out the GitHub, wiki and journal article.

DRAM annotate assemblies

In KBase Assembly objects contain nucleotide sequences from genomes or metagenomes. DRAM can predict genes and annotate their function from KBase Assembly objects which may be microbial isolate genomes, metagenome assembled genomes or metagenomes. This is done with the Annotate and Distill Assemblies with DRAM app. This app can also anntoate AssemblySet objects which contain collection of Assembly objects. It also generates a Genome object and a GenomeSet object which can be used for further analysis with other KBase apps. The full annotations and other DRAM files are also available for download in the app.

NOTE: When annotating metagenomes we do not want to generate genome objects as it takes a long time and will not be useful. So when annotating metagenomes please check the "Is metagenome?" checkbox.

Single assembly

Here we show DRAM annotation of a single assembly. This is an Assembly object from an E. coli genome. You can see that a Genome object is. You also get to see the DRAM product in the app output. Hover over the heatmap to see which genes were present that suggest the ability of this organism to do these functions.

Ecoli_refseq_assembly
v5 - KBaseGenomeAnnotations.Assembly-5.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/88325
Annotate and Distill Assemblies with DRAM
Annotate your assembly with DRAM. Annotations will then be distilled to create an interactive functional summary per assembly.
This app produced errors.
No output found.

Assembly set

To annotate multiple genomes at once you can use a KBase AssemblySet. Here we build an AssemblySet from the same E. coli genome as well as a Rhodobacter genome. Then we use this as input to Annotate and Distill Assemblies with DRAM. Look at how we can then use the product, displayed in the app, to see the differences in genomic potential between these two organisms.

Rhodo_auto_contigs
v2 - KBaseGenomeAnnotations.Assembly-4.1
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/88325
Build AssemblySet - v1.0.1
Allows users to create an AssemblySet object.
This app completed without errors in 20s.
Objects
Created Object Name Type Description
ecoli_rhodo_assembly_set AssemblySet KButil_Build_AssemblySet
Summary
assembly objs in output set ecoli_rhodo_assembly_set: 2
Annotate and Distill Assemblies with DRAM
Annotate your assembly with DRAM. Annotations will then be distilled to create an interactive functional summary per assembly.
This app produced errors.
No output found.

DRAM annotate genomes

KBase Genome objects are the genome of an organism with predicted, and optionally annotated, protein sequences. These can be annoated by DRAM with the Annotate and Distill Genomes with DRAM app. You can also use GenomeSets, collections of Genome objects, as input. This is equivalent to using DRAM.py annotate_genes with the command line version of DRAM. When you use this app the Genome or GenomeSet will be updated to include DRAM annotations. The full annotations and other DRAM files are also available for download in the app.

Single genome annotation

Here we are annotating a Genome object built from a E. coli genome. This also shows how you can merge the annotations KO and EC annotations from DRAM into a single set of anntoations within a Genome object and buid a metabolic model with them.

NOTE: When building metabolic models you must uncheck the "Include nontemplate reactions" box to use the DRAM annotations. We also suggest using nontemplate reactions and considering using not mass/charge balanced reactions.

Escherichia_coli_K-12_MG1655
v5 - KBaseGenomes.Genome-11.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/88325
Annotate and Distill Genomes with DRAM
Annotate your genome(s) with DRAM. Annotations will then be distilled to create an interactive functional summary per genome.
This app completed without errors in 38m 2s.
Report
Summary
Here are the results from your DRAM run.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/88325
  • annotations.tsv - DRAM annotations in a tab separate table format
  • genes.faa - Genes as amino acids predicted by DRAM with brief annotations
  • product.tsv - DRAM product in tabular format
  • metabolism_summary.xlsx - DRAM metabolism summary tables
  • genome_stats.tsv - DRAM genome statistics table
Merge Metabolic Annotations
Merge multiple metabolic annotations into a single merged annotation based on thresholds
This app completed without errors in 2m 11s.
Objects
Created Object Name Type Description
Escherichia_coli_K-12_MG1655_merged Genome Genome with merged annotations
Report
Links
Output from Merge Metabolic Annotations
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/88325
Build Metabolic Model
Construct a draft metabolic model based on an annotated genome.
This app completed without errors in 3m 30s.
Objects
Created Object Name Type Description
Escherichia_coli_K-12_MG1655_merged_model FBAModel FBAModel-14 Escherichia_coli_K-12_MG1655_merged_model
Escherichia_coli_K-12_MG1655_merged_model.gf.1 FBA FBA-13 Escherichia_coli_K-12_MG1655_merged_model.gf.1
Report
Summary
RefGlucoseMinimal media.
Output from Build Metabolic Model
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/88325

Genome set annotation

DRAM can also annotate GenomeSets which are collections of Genome objects. Here we are going to make a genome set from the E. coli genome as well as a Shewanella genome and annotate it with DRAM. This makes it easy to compare the genomic potential of the two organisms.

Shewanella_oneidensis_MR-1
v3 - KBaseGenomes.Genome-11.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/88325
Build GenomeSet - v1.7.6
Allows users to create a GenomeSet object.
This app completed without errors in 46s.
Objects
Created Object Name Type Description
ecoli_soneidensis_genome_set GenomeSet KButil_Build_GenomeSet
Summary
genomes in output set ecoli_soneidensis_genome_set: 2
Annotate and Distill Genomes with DRAM
Annotate your genome(s) with DRAM. Annotations will then be distilled to create an interactive functional summary per genome.
This app completed without errors in 1h 13m 4s.
Report
Summary
Here are the results from your DRAM run.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/88325
  • annotations.tsv - DRAM annotations in a tab separate table format
  • genes.faa - Genes as amino acids predicted by DRAM with brief annotations
  • product.tsv - DRAM product in tabular format
  • metabolism_summary.xlsx - DRAM metabolism summary tables
  • genome_stats.tsv - DRAM genome statistics table

DRAM-v

You can also annotate viruses with a view toward finding auxiliary metabolic genes (AMGs) using DRAM. To do this you must start with a metagenomic assembly. Here we are using one from a previously published study. First you must detect viruses in your assembly using the VirSorter KBase app. Then in the VirSorter Summary in the results tab there will be a shock ID. To then run DRAM-v you use one of the Virus files that come from VirSorter along with the shock ID from the VirSorter Summary. After you annotate this with the Annotate and Distill Viral Assemblies with DRAM-v app you will get a interactive heatmap showing the AMGs present in these viruses. The full annotations and other DRAM files are also available for download in the app.

Import FASTA File as Assembly from Staging Area
Import a FASTA file from your staging area into your Narrative as an Assembly data object
This app completed without errors in 4m 49s.
Objects
Created Object Name Type Description
emerson_viruses.fasta_assembly Assembly Imported Assembly
Summary
The uploaded assembly has too many contigs to display here. Click on the object for a dedicated viewer
emerson_viruses.fasta_assembly
v1 - KBaseGenomeAnnotations.Assembly-5.0
The viewer for the data in this Cell is available at the original Narrative here: https://narrative.kbase.us/narrative/88325
VirSorter 1.0.5
Identifies viral sequences from viral and microbial metagenomes
This app completed without errors in 2h 31m 39s.
Objects
Created Object Name Type Description
VirSorter-Category-2 Assembly KBase Assembly object from VIRSorter
VirSorter-Category-1 Assembly KBase Assembly object from VIRSorter
VirSorter-Category-3 Assembly KBase Assembly object from VIRSorter
VirSorter-Category-6 Assembly KBase Assembly object from VIRSorter
VirSorter-Category-4 Assembly KBase Assembly object from VIRSorter
VirSorter-Category-5 Assembly KBase Assembly object from VIRSorter
VirSorter_binnedContigs BinnedContigs BinnedContigs from VIRSorter
Report
Summary
Here are the results from your VIRSorter run. Above, you'll find a report with all the identified (putative) viral genomes, and below, links to the report as well as files generated. For DRAM-v users, the shock ID for the affi contigs file is: 3f21f9a4-ebb1-403c-940b-2374577fb525
Links
  • index.html - HTML summary report for VIRSorter-predicted viral genomes.
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/88325
  • VIRSorter_predicted_viral_fna.tar.gz - FASTA-formatted nucleotide sequences of VIRSorter predicted viruses
  • VIRSorter_predicted_viral_gb.tar.gz - Genbank-formatted sequences of VIRSorter predicted viruses
Output from VirSorter 1.0.5
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/88325
Annotate and Distill Viral Assemblies with DRAM-v
Annotate your viral assembly with DRAM. Annotations will then be distilled to create an interactive functional summary per virus.
This app completed without errors in 2h 37m 20s.
Report
Summary
Here are the results from your DRAM run.
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/88325
  • annotations.tsv - DRAM annotations in a tab separate table format
  • genes.fna - Genes as nucleotides predicted by DRAM with brief annotations
  • genes.faa - Genes as amino acids predicted by DRAM with brief annotations
  • genes.gff - GFF file of all DRAM annotations
  • trnas.tsv - Tab separated table of tRNAs as detected by tRNAscan-SE
  • genbank.tar.gz - Compressed folder of output genbank files
  • amg_summary.tsv - DRAM-v AMG summary table
  • vMAG_stats.tsv - DRAM-v vMAG statistics table

Released Apps

  1. Annotate and Distill Assemblies with DRAM
    • DRAM source code
    • DRAM documentation
    • DRAM publication
  2. Annotate and Distill Genomes with DRAM
    • DRAM source code
    • DRAM documentation
    • DRAM publication
  3. Annotate and Distill Viral Assemblies with DRAM-v
    • DRAM source code
    • DRAM documentation
    • DRAM publication
  4. Import FASTA File as Assembly from Staging Area
    no citations
  5. Merge Metabolic Annotations
    • [1] Griesemer M, Kimbrel JA, Zhou CE, Navid A, D'haeseleer P. Combining multiple functional annotation tools increases coverage of metabolic annotation. BMC Genomics. 2018 Dec 19;19(1):948. doi: 10.1186/s12864-018-5221-9.
    • [2] Hanson AD, Pribat A, Waller JC, de Cr cy-Lagard V. Unknown proteins and orphan enzymes: the missing half of the engineering parts list - and how to find it. Biochem J. 2010;425:1 11. doi: 10.1042/BJ20091328.
    • [3] Ijaq J, Chandrasekharan M, Poddar R, Bethi N, Sundararajan VS. Annotation and curation of uncharacterized proteins- challenges. Front Genet. 2015;6:1750. doi: 10.3389/fgene.2015.00119.
    • [4] Land M, Hauser L, Jun S-R, Nookaew I, Leuze MR, Ahn T-H, et al. Insights from 20 years of bacterial genome sequencing. Funct Integr Genomics. 2015;15:141 161. doi: 10.1007/s10142-015-0433-4.
    • [5] Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, Mundy M, Chia N, Noor E, Beber ME, Best AA, DeJongh M, Kimbrel JA, D'haeseleer P, McCorkle SR, Bolton JR, Pearson E, Canon S, Wood-Charlson EM, Cottingham RW, Arkin AP, Henry CS. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res. 2021 Jan 8;49(D1):D1555. doi: 10.1093/nar/gkaa1143.

Apps in Beta

  1. Build AssemblySet - v1.0.1
    • Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology. 2018;36: 566. doi: 10.1038/nbt.4163
  2. Build GenomeSet - v1.7.6
    • Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology. 2018;36: 566. doi: 10.1038/nbt.4163
  3. Build Metabolic Model
    • [1] Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28: 977 982. doi:10.1038/nbt.1672
    • [2] Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42: D206 D214. doi:10.1093/nar/gkt1226
    • [3] Latendresse M. Efficiently gap-filling reaction networks. BMC Bioinformatics. 2014;15: 225. doi:10.1186/1471-2105-15-225
    • [4] Dreyfuss JM, Zucker JD, Hood HM, Ocasio LR, Sachs MS, Galagan JE. Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM. PLOS Computational Biology. 2013;9: e1003126. doi:10.1371/journal.pcbi.1003126
    • [5] Mahadevan R, Schilling CH. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng. 2003;5: 264 276.
  4. VirSorter 1.0.5
    • Roux S, Enault F, Hurwitz BL, Sullivan MB. (2015). VirSorter: mining viral signal from microbial genomic data. PeerJ 3:e985.